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Free device location independent WiFi-based localisation using received signal strength indicator and channel state information

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IET WIRELESS sensor SYSTEMS 2023年第5期13卷 163-177页

作者： Abuhoureyah, Fahd Chiew, Wong Yan Isira, Ahmad Sadhiqin Bin Mohd Al-Andoli, Mohammed Univ Teknikal Malaysia Melaka UTeM Ctr Telecommun Res & Innovat CeTRI Fak Kejuruteraan Elekt dan Kejuruteraan Komputer F Melaka Malaysia Multimedia Univ MMU Fac Engn & Technol Melaka Malaysia

The trajectory localisation of human activities using signal analytics has become a reality due to the widespread use of advanced signal processing systems. Device-free localisation using WiFi devices is prevalent, and the received signal strength indicator (RSSI) and channel state information (CSI) signals offer additional benefits. However, radio frequency (RF) localisation is highly dependent on the environment, so updating fingerprint data is necessary by changing the environment. This work presents Fine-grained Indoor Detection and Angular Radar for recognising and locating humans using a multipath trajectory reflections system that does not require training. It estimates location using a probabilistic approach that considers changes in CSI and RSSI across multiple nodes, generating an informative dataset that reflects the current trajectory and status of the location. The presented method extracts data from clustered Raspberry Pi 4B and Nexmon. The method exhibits a versatile real-time location-tracking solution by utilising the distinctive properties of RF signals. This technology has significant implications for various applications, including human medical monitoring, gaming, smart cities, and optimising building layouts to improve efficiency. The model demonstrates location-independent localisation with up to 80% accuracy in mapping trajectories at any location. The findings indicate that the proposed model is effective and reliable for indoor localisation and activity tracking, making it a promising solution for implementation in real-world environments.

关键词： body sensor networks intelligent sensors signal detection

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Research on environmental sensor data analysis based on real-time data warehouse

Research on environmental sensor data analysis based on real...

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2024 International conference on Cloud Computing, Performance Computing, and Deep Learning, CCPCDL 2024

作者： Lv, Taizhi Zou, Yujuan Wang, Yaowei Wu, Enze College of Information Engineering Jiangsu Maritime Institute Nanjing211170 China Nanjing Haizhirui Information Technology Co. Ltd. Nanjing210001 China

ISBN: (纸本)9781510683044

Environmental sensors play a crucial role in modern society. They provide strong support for environmental protection, resource management, and sustainable development by monitoring and perceiving various parameters in real-time. With the development of big data and artificial intelligence technologies, intelligent management of environmental data has become possible. To enhance the level of intelligence in environmental monitoring, this paper studies the performance of environmental sensor data operations in data warehouses such as Hive, ClickHouse, and Doris, and constructs an environmental sensor data analysis system based on a real-time data warehouse. The system employs Flink for real-time collection of environmental data, ClickHouse for data storage, Spark for data analysis, and front-end and back-end technologies for data visualization. The system achieves automatic collection, processing, and analysis of environmental data, thereby improving the efficiency and accuracy of environmental monitoring. © 2024 SPIE.

关键词： Environmental monitoring

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MOSIC: MULTIMODAL SEMANTIC INTEGRATED COMMUNICATION FOR HEALTH MONITORING IN IOT SCENARIOS 49

MOSIC: MULTIMODAL SEMANTIC INTEGRATED COMMUNICATION FOR HEAL...

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49th IEEE International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yang, Minxi Gao, Dahua Li, Jiaxuan Xu, Wenlong Song, Xiaodan Shi, Guangming Xidian Univ Sch Artificial Intelligence Xian Shaanxi Peoples R China Peng Cheng Lab Shenzhen Guangdong Peoples R China Pazhou Lab Huangpu Guangdong Peoples R China Xidian Univ Guangzhou Inst Technol Guangzhou Guangdong Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Monitoring multimodal signals provides a more comprehensive understanding of health conditions compared to single-mode monitoring. In the face of the significant volumes of multimodal signals, existing IoT health monitoring systems primarily focus on high-fidelity signal transmission by encoding multimodal signals separately. However, due to the lack of consideration for the downstream applications and correlation between multimodal signals, a portion of bandwidth resources is wasted on task-irrelevant information and inter-modal redundancy. To address this issue, we propose the Multimodal Semantic Integration Communication (MoSIC) framework composed of three levels: At the sensor level, multiple wearable sensors collect and send different modal signals to a mobile terminal;at the mobile terminal level, the terminal employs deep source-channel joint encoding for the received multimodal signals, extracting single-modal embedded features using a backbone network, and obtaining cross-modal features through a feature fusion network with contrastive constrain;at the cloud level, a decoding network symmetric to the encoding network reconstructs the multimodal signals, which are then used for downstream applications such as human activity recognition. MoSIC focuses on semantically integrating multimodal signals for downstream applications, resulting in improved encoding and transmission efficiency. It also reduces the radio-frequency power consumption and bandwidth requirements.

关键词： Health monitoring Internet of Things semantic communication multimodal signal transmission physiological signal processing

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Smart Distributed Parking System using Internet of Things 6

Smart Distributed Parking System using Internet of Things

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6th International conference on intelligent Computing and Control Systems, ICICCS 2022

作者： Leelipushpam Paulraj, Getzi Jeba Johnraja Jebadurai, Immanuel Khan, Pathan Firoz Sumanth, Neela Reddy, Kothapally Vishal Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665410359

With the growth of cities and population, urban corporation faces potential challenges. One such challenge is regulating parking spaces. This become a tedious job during weekends and festival season. Improper regulation in parking spaces lead to traffic congestion and affects the fuel economy. Various parking solutions using sensor networks and image processing techniques have been proposed in literatures. For developing smart city environment, single parking space management is not sufficient and image processing based parking system can be applied only to large spaces. This paper proposes distributed parking management system. Three parking slots have been created. The IR sensors capture the status of the space and communicate to the cloud. The user can also check the real time parking space availability in mobile application. Prototype has been developed and checked under various condition. The proposed solution provides parking space information on real time and improves fuel efficiency. © 2022 IEEE.

关键词： sensor networks

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Exploring Deep Learning Models for the Identification and Segmentation of Leaf Diseases 4

Exploring Deep Learning Models for the Identification and Se...

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4th International conference on Ubiquitous Computing and intelligent information Systems, ICUIS 2024

作者： Nithiya, A. Mahendran, N. M.Kumarasamy College of engineering Department of Information Technology Karur India M.Kumarasamy College of Engineering Department of Electronics and Communication Engineering Karur India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

Agriculture plays a main role in our economy. Amid the worsening of agricultural problems, prompt and accurate identification of illnesses connected to plants has become a very important component of proper crop development and obtaining maximum yield. The current study deals with the practice of machine learning and deep learning methods in the Identification of diseases using the image data from the mobile and drone cameras and along with these data the environmental data that are acquired through IoT sensors. In order to make crop production sustainable, it is imperative to promptly and accurately diagnose unhealthy plants due to the rapidly expanding agricultural issues through algorithms such as Convolutional Neural networks (CNNs), Dense Net, etc. in predicting the disease. The different data processing methods such as image enhancement, normalization, and selection of features like Principal Component Analysis (PCA) and Gated Recurrent Multi-attention neural network (GRMA-Net), which are used to optimize the productivity of disease detection. Moreover, it looks into the impact of data fusion from the images and sensors on the classification accuracy. The different classifiers such as Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) and compares with an advanced techniques like Resnet50, VGG, Efficient Net etc., on the basis of certain statistical indicators. This study draws attention to the merits of associating the information obtained from IoT to Deep learning which as a result will increase farming productivity and result in fewer chemicals used. The outcomes indicate the great possibility of the treatment of a disease in early stage, the decreasing of the use of chemicals, and the increment of the security of the global food supply at the long-term. © 2024 IEEE.

关键词： Network security

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A Review on the need of Clustering Techniques Used for Wireless sensor networks 2

A Review on the need of Clustering Techniques Used for Wirel...

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2nd International conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Bharany, Salil Rehman, Ateeq Ur Tariq Sadiq, M. Farrukh, Muhammad Alharbi, Meshal Hussain, Amjad Issa, Ghassan F. CET and Technology Department Amritsar India Government College University Department of Electrical Engineering Lahore Pakistan University of Brighton School of Architecture & Engineering United Kingdom Minhaj University Department of Computer Science Lahore Pakistan Prince Sattam Bin Abdulaziz University College of Computer Engineering and Sciences Department of Computer Science Alkharj11942 Saudi Arabia School of Computer Science NCBA&E Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan Skyline University College School of Information Technology Sharjah United Arab Emirates

ISBN: (纸本)9798350335644

Wireless sensor networks have been implemented in various software to help gather and analyze data from the physical world. Wireless sensor networks are networks of multiple tiny sensors powered by low-energy batteries (WSNs). WSN lifetime is a critical factor. This is because, after WSNs have been deployed in a given application, the inaccessibility of the sensing nodes makes it impossible to recharge or replace the power source that is limited in energy. Until recently, WSNs were thought to be the same. All the network nodes have the same power, processing speed, and operational capacity. However, scientists have developed heterogeneous WSNs, in which the properties of individual nodes may vary, to extend the lifespan of networks. In order to extend a network's helpful life, energy-efficient protocols must be designed. Even though WSN has its own problems, it has brought a fresh perspective to studying real-time intelligent systems. WSN (Wireless sensor Network) has gained significant popularity in the recent years, owing to its vast applications in real-time scenarios, such as disaster management, pollution monitoring, temperature monitoring, traffic monitoring, transport monitoring, healthcare monitoring, battlefield monitoring, and border security surveillance, to name a few. Numerous sensor nodes are employed in these applications. They are typically placed in the sensing field, where they automatically transfer data to the base station (BS) through energy transmission (i.e., battery in the sensor). In WSN, clustering is one method for optimizing power consumption at the sensor node. The sensor nodes are grouped and given a leader when they undergo clustering. This study overviews various WSN clustering algorithms, each categorized according to various clustering properties. © 2023 IEEE.

关键词： sensor nodes

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TODOS: Thermal sensor Data-driven Occupancy Estimation System for Smart Buildings 23

TODOS: Thermal sensOr Data-driven Occupancy Estimation Syste...

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10th ACM International conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys)

作者： Rajabi, Hamid Ding, Xianzhong Du, Wan Cerpa, Alberto Univ Calif Merced Merced CA 95343 USA

ISBN: (纸本)9798400702303

Occupancy sensing and estimation in large commercial buildings has become a significant problem to be solved, with applications ranging from occupancy-based HVAC control to space planning, and security, etc. Thermal sensing is a promising technology to solve this problem, being easy to deploy in practice and allowing an actual occupancy count in a particular room without violating the data and privacy concerns. While initial strides have been made to solve this problem with thermal arrays, there are many problems that remain unsolved, including accuracy performance, overlapping of sensing areas that lead to under/over-counting, and data training requirements for different zones. In this paper, we introduce TODOS1, a novel system for estimating occupancy in intelligent buildings. TODOS uses a low-cost, low-power thermal sensor array along with a passive infrared sensor. We introduce a novel data processing pipeline that allows us to automatically extract features from the thermal images using an artificial neural network. Through an extensive experimental evaluation(2), we show that TODOS provides occupancy detection accuracy of 98% to 100% under different scenarios. In addition, it solves the issue of occupancy over/under-counting by overlapping sensing areas when using multiple thermal sensors in large rooms. This is done by treating the entire area as a single input thermal image instead of partitioning the area into multiple thermal images individually processed. Furthermore, TODOS introduces a data augmentation technique that allows the generation of training data for rooms of different sizes and shapes, without requiring specific training data from each room. Using these data, TODOS can train specifically designed neural networks optimized for any room size and shape, and achieve almost the same level of occupancy detection accuracy in rooms where experimental labeled training data is available, making it a viable solution that generalizes to the different room

关键词： smart buildings occupancy estimation HVAC systems thermal occupancy sensor neural networks data augmentation

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Integrating Reconfigurable Foot Design, Multi-modal Contact Sensing, and Terrain Classification for Bipedal Locomotion 3

Integrating Reconfigurable Foot Design, Multi-modal Contact ...

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3rd Modeling, Estimation and Control conference (MECC)

作者： Tyler, Ted Malhotra, Vaibhav Montague, Adam Zhao, Zhigen Hammond, Frank L., III Zhao, Ye Georgia Inst Technol George W Woodruff Sch Mech Engn Atlanta GA 30309 USA

ISBN: (纸本)9781713872344

The ability of bipedal robots to adapt to diverse and unstructured terrain conditions is crucial for their deployment in real-world environments. To this end, we present a novel, bio-inspired robot foot design with stabilizing tarsal segments and a multifarious sensor suite involving acoustic, capacitive, tactile, temperature, and acceleration sensors. A real-time signal processing and terrain classification system is developed and evaluated. The sensed terrain information is used to control actuated segments of the foot, leading to improved ground contact and stability. The proposed framework highlights the potential of the sensor-integrated adaptive foot for intelligent and adaptive locomotion.

关键词： Mechatronic Systems Robotics Modeling Identification and Signal processing

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FPGA-Based Analysis of AODV Routing in Wireless sensor networks

FPGA-Based Analysis of AODV Routing in Wireless Sensor Netwo...

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2023 International conference on Power Energy, Environment and intelligent Control, PEEIC 2023

作者： Alam, Intekhab Raja Praveen, K.N. Jangid, Mahesh Kumar Maharishi School of Engineeing & Technology Maharishi University of Information Technology Uttar Pradesh India Computer Science Department Bangalore India Jaipur India

ISBN: (纸本)9798350357769

The continuous collection of physiological data, especially temperature and pressure, throughout the human body, using wireless, skin-adherent detectors based on NFC (near-field communication) technology, is presented in this research study. To better understand human health, forecast illnesses, and avoid problems like pressure ulcers, the article stresses the significance of monitoring skin temperature as well as pressure in diverse anatomical locations. This study proposes a novel NFC-based method for continuous physiological monitoring. It provides hitherto unattainable tracking capabilities for pressure and temperature throughout the human body and has the potential to significantly enhance healthcare results. In order to increase the radius and sensitivities of these sensors, future research will examine enlarged clinical investigations and developments in wireless technology. © 2023 IEEE.

关键词： Continuous monitoring and Health outcomes Healthcare NFC technology Physiological monitoring Pressure sensing element Pressure ulcers Skin temperature Temperature measurement Wireless sensors

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GRUI: A Novel Gesture Recognition Utilizing UWB sensor and IMU

GRUI: A Novel Gesture Recognition Utilizing UWB Sensor and I...

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2024 IEEE International conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lee, Dongjae Yoo, Kyeonghyun Jung, Wooyong Kim, Hwangnam Korea University Department of Electrical Engineering Seoul Korea Republic of

ISBN: (纸本)9781665410205

In recent advancements in sensor and artificial intelligence technologies, the reliability of gesture recognition has significantly improved, prompting various industrial fields to adopt this technology. However, most gesture recognition systems rely on optical methods because of their high accuracy, despite require complex and computationally intensive processes. Moreover, these systems are associated with high construction costs and are susceptible to environmental factors. This paper introduces a novel gesture recognition system, which effectively tracks and estimates gestures using cost-effective ultra-wideband (UWB) sensors and inertial measurement units (IMU). The system acquires position data of gesture through UWB sensors and includes essential data processing steps such as the detection and removal of abnormal data via IMU, data smoothing with a Kalman filter, and data normalization and scaling. Notably, normalization and scaling are achieved by converting the position data into grayscale images, ensuring the consistency of data features and enhancing gesture recognition accuracy across diverse users. The proposed system employs a convolutional neural network (CNN) model to estimate gestures from these images. Comparative analyses demonstrate that the proposed system exhibits superior gesture classification performance compared to systems utilizing a long-short term memory (LSTM) model and those employing the same CNN model without the aforementioned data processing steps. Therefore, this system is not only cost-effective but also efficiently tracks and estimates gestures, offering significant improvements over existing methods. © 2024 IEEE.

关键词： Convolutional neural networks

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